For FL, there are two main classifications: federated reinforcement learning (FRL) and
federated supervised learning (FSL).
In [1]}, the goal of FRL is to enable wireless devices to remember what they have learned and what
other wireless devices have learned.
FRL can be used in the case where multiple wireless devices make decisions in different environments.
In FRL, each wireless device builds a learning network with the help of other wireless devices.
